Estimation of the Burr-III Distribution Parameters Based on the Dual Generalized Progressive Hybrid Censoring Scheme

In reliability theory, the Bayes’ method based on the informative prior is the most applicable and more efficient than the other estimation methods in reliability analysis. Therefore, the main objective of this work is to introduce optimal numerical iteration techniques, such as the Picard and Runge- Kutta methods, which are more efficient and compatible with the Bayes’ method. The proposed methods have been used for estimating the Burr-III distribution parameters and compared to Bayes’ method via an extensive Monte Carlo simulation. The simulation results indicated that the Picard and Runge-Kutta methods provide better estimates and outperform the Bayes’ method estimates based on the dual generalized progressive hybrid censoring scheme. Finally, two real datasets have been analyzed for illustrations and comparison of the proposed methods.